UNICEF Data Story

UNICEF Data Story: Exploring Global Trends in Child Welfare and Aging Populations: A Data-Driven Approach Using UNICEF Indicators

<p style="font-size: 1.3em; font-weight: bold; margin-bottom: 0.2em;">BAA1030 Data Analytics & Story Telling</p>
<p style="font-size: 1.0em;">Module Code: 20074</p>
<p style="font-size: 1.1em; margin-top: 1em;"><b>Author:</b> Tushar Goradia</p>
<p style="font-size: 1.1em;">Student ID: A00047860</p>
<p style="font-size: 1.1em;">Programme: MSc in Management (Strategy)</p>

Executive Summary

This report presents an analysis of global demographic trends using UNICEF child mortality data and old-age dependency ratios. Through data cleaning, statistical analysis, and interactive visualizations, key patterns were identified. Results show that child mortality remains highest in low-income regions, while aging populations are more prominent in developed countries. The findings highlight the urgent need for targeted health interventions and long-term demographic planning.

#3 Introduction

This report explores global demographic patterns through two key indicators: child mortality rates and old-age dependency ratios. The goal is to raise public awareness about persistent inequalities in child health and the growing challenges of aging populations worldwide.

Global health trends have shown remarkable progress in recent decades, yet deep inequalities remain. In this report, we focus on two critical areas: child vaccination rates and old-age dependency, examining how countries, particularly India, are navigating these demographic challenges.

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import matplotlib.pyplot as plt

import pandas as pd
from plotnine import *
import geopandas as gpd

indicator1 = pd.read_csv('/content/drive/MyDrive/unicef_indicator_1.csv')
indicator2 = pd.read_csv('/content/drive/MyDrive/unicef_indicator_2.csv')
metadata = pd.read_csv('/content/drive/MyDrive/unicef_metadata.csv')

Vaccination Rate Over Time in India

We begin by looking at India’s progress in child immunization over time. The Vaccination Rate Over Time chart reveals a steady rise, reflecting the success of nationwide public health campaigns. However, while these rates are improving, economic factors still play a vital role in health outcomes.

This interactive line chart shows how vaccination rates in India have changed over time. Markers highlight yearly values, revealing major increases in child immunization efforts across the country.

!pip install plotly
import plotly.express as px
import pandas as pd

indicator2 = pd.read_csv('/content/drive/MyDrive/unicef_indicator_2.csv')

india_data = indicator2[indicator2['country'] == 'India']

fig = px.line(
    india_data,
    x='time_period',
    y='obs_value',
    title='Vaccination Rate Over Time (India)',
    labels={'time_period': 'Year', 'obs_value': 'Vaccination %'},
    markers=True
)

fig.update_traces(
    hovertemplate='Year: %{x}<br>Vaccination %: %{y}'
)

fig.update_layout(
    xaxis_rangeslider_visible=True,
    updatemenus=[
        dict(
            type="buttons",
            direction="left",
            buttons=list([
                dict(
                    args=[{"xaxis.range": [india_data['time_period'].min(), india_data['time_period'].max()]}],
                    label="Reset",
                    method="relayout"
                ),
                dict(
                    args=[{"xaxis.autorange": True}],
                    label="Auto",
                    method="relayout"
                )
            ]),
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.11,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.show()

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GDP per Capita vs Life Expectancy in India

Next, we explore the relationship between economic prosperity and life expectancy. The GDP per Capita vs Life Expectancy scatterplot clearly shows that as India’s wealth grew, life expectancy also rose — although gaps remain compared to more developed nations.

This scatter plot explores the relationship between India’s GDP per capita and life expectancy over time. A linear trendline shows how improvements in economic conditions have correlated with increases in life expectancy.

import plotly.express as px
import pandas as pd

india_data = metadata[metadata['country'] == 'India'].dropna(subset=['GDP per capita (constant 2015 US$)', 'Life expectancy at birth, total (years)'])

fig = px.scatter(
    india_data,
    x='GDP per capita (constant 2015 US$)',
    y='Life expectancy at birth, total (years)',
    title='GDP per Capita vs Life Expectancy (India)',
    hover_data=['year'],
    trendline="ols",
    trendline_color_override="red"
)

fig.update_traces(
    hovertemplate='Year: Over the Years<br>GDP: %{x}<br>Life Expectancy: %{y}'
)

fig.show()

Old Age Dependency Ratio in India Since 2000 (by Gender)

As populations live longer, a new concern emerges: aging societies. The Old Age Dependency Ratio in India grouped bar chart highlights how the burden of supporting elderly citizens has increased steadily since 2000 — across both genders. This demographic shift brings significant economic and social implications for the future.

This grouped bar chart displays how India’s old-age dependency ratio has evolved since 2000, separately for males, females, and the total population. It highlights changing gender patterns as the population ages.

!pip install pandas plotnine geopandas plotly
import matplotlib.pyplot as plt
import pandas as pd
from plotnine import *
import geopandas as gpd
import plotly.express as px

indicator_name = 'Old age dependency ratio'
india_data = indicator1[
    (indicator1['indicator'] == indicator_name) &
    (indicator1['country'] == 'India') &
    (indicator1['time_period'] >= 2000)
]

fig = px.bar(
    india_data,
    x='time_period',
    y='obs_value',
    color='sex',
    title=f'{indicator_name} in India (From 2000)',
    labels={'time_period': 'Year', 'obs_value': 'Value (in %)'},
)

fig.update_layout(barmode='group')
fig.show()

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Global Old Age Dependency Ratio

Zooming out to the global scale, the Old Age Dependency Ratio World Globe shows that aging populations are not just a local issue. Countries like Japan, Italy, and Germany face the highest old-age dependency burdens, demanding urgent reforms in pensions, healthcare, and workforce planning.

This interactive globe visualizes old-age dependency ratios across countries. Darker red shades indicate higher elderly dependency relative to the working-age population, highlighting regions most affected by aging trends.

!pip install plotly
import plotly.express as px
import pandas as pd
import geopandas as gpd

world = gpd.read_file('/content/drive/MyDrive/World Map')
indicator_data = pd.read_csv('/content/drive/MyDrive/unicef_indicator_1.csv')

indicator_name = 'Old age dependency ratio'
indicator_data = indicator_data[indicator_data['indicator'] == indicator_name]

merged_data = world.merge(indicator_data, left_on='NAME', right_on='country', how='left')

fig = px.choropleth(
    merged_data,
    geojson=merged_data.geometry,
    locations=merged_data.index,
    color='obs_value',
    color_continuous_scale='Reds',
    title=f'World Map of {indicator_name}',
    projection='orthographic',
    hover_data=['NAME']
)

fig.update_traces(
    hovertemplate='<b>%{customdata[0]}</b><br>' +
                  'Old Age Dependency Ratio: %{z}<extra></extra>',
    customdata=merged_data[['NAME']]
)

fig.update_geos(
    visible=False,
    showcountries=True,
    countrycolor="black",
    showocean=True,
    oceancolor="lightblue",
    showlakes=True,
    lakecolor="lightblue",
    showrivers=True,
    rivercolor="lightblue"
)

fig.update_layout(
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type="orthographic"
    )
)

fig.show()

Output hidden; open in https://colab.research.google.com to view.

Latest Vaccination Rates Worldwide

Finally, we turn again to child health. The Latest Vaccination Rates World Map starkly highlights that many countries — especially in Africa and parts of Asia — still struggle to provide basic vaccinations. While global averages improve, millions of children remain vulnerable due to unequal access to healthcare.

This interactive world map displays the most recent child vaccination rates across countries. The visualization highlights global inequalities in healthcare access and immunization coverage.

import plotly.express as px
import pandas as pd


indicator2 = pd.read_csv('/content/drive/MyDrive/unicef_indicator_2.csv')

latest_vaccination_rates = indicator2.groupby('country')['obs_value'].last().reset_index()

fig = px.choropleth(
    latest_vaccination_rates,
    locations='country',
    locationmode='country names',
    color='obs_value',
    hover_name='country',
    color_continuous_scale='Viridis',
    title='Latest Vaccination Rates by Country',
    labels={'obs_value': 'Vaccination Rate'}
)

fig.update_layout(
    geo=dict(showframe=False, showcoastlines=False, projection_type='equirectangular')
)

fig.show()

Through this journey, the data tells a powerful story: As countries work to protect their youngest citizens, they must also prepare to care for their oldest. Balancing these priorities will define the next generation of global health and social policy.

Conclusion

This report highlights the complex but interconnected trends shaping global health and demographic change. In India, steady improvements in vaccination rates demonstrate the success of public health initiatives, contributing to longer life expectancies and better child survival outcomes. However, as life expectancy rises, India, like many countries worldwide, faces the growing challenge of supporting an aging population.

Our global visualizations further emphasize these dual pressures. While vaccination coverage has expanded in many parts of the world, significant disparities remain, particularly in low-income regions. Simultaneously, developed nations are contending with rising old-age dependency ratios, reshaping social, economic, and healthcare priorities.

Addressing these challenges will require a balanced approach: investing in both child health and elderly care, while recognizing the underlying economic factors that influence health outcomes. The future of public health will depend not only on curing disease but on building resilient, inclusive systems that serve populations across every stage of life.

Data Sources

The data used in this report is sourced from UNICEF global indicators datasets provided for the BAA1030 Data Storytelling assignment. Additional metadata was used to enhance the demographic analysis.